Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 4,5 and 6. This lessons covers how to create line graphs and scatterplots in Excel, specifically, how to make standard changes to line graphs and scatterplots how to plot a line graph without date value variables how to add data labels how to amend data points Lesson content, A PowerPoint/PDF presentation, ‘Practise creating graphs in Excel (part 2)’ Excel Question workbook on ‘Practise creating graphs in Excel (part 2)’ (for learners) Excel Answers workbook on ‘Practise creating graphs in Excel (part 2)’ (for teachers) Planning document with learning intentions and success criteria For more information on the Data Science NPA, please see teachdata.science If you have any questions or feedback on this lesson, please email lessons@effini.com This lesson has been created by effini in partnership with Data Education in Schools, The Data Lab and Data Skills for Work, with funding from the Scottish Government. © 2022. This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 4,5 and 6. This lesson follows on from ‘The Analysis Process’ which is available from the effini TES shop. This lesson covers the data understanding part of the analysis process in Excel, specifically, • what is metadata and the importance of a data dictionary • how to identify the shape and size of a dataset in Excel and data types of variables • how to identify missing values and outliers in Excel Lesson content, A PowerPoint/PDF presentation, ‘Dataset understanding in Excel’ Excel Question workbook on ‘Dataset understanding in Excel’ (for learners) Excel/PDF Answers workbook on ‘Dataset understanding in Excel’ (for teachers) Planning document with learning intentions and success criteria For more information on the Data Science NPA, please see teachdata.science This lesson has been created by effini in partnership with Data Education in Schools, The Data Lab and Data Skills for Work, with funding from the Scottish Government. © 2022. This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Level 6. This lesson covers how to manage data, specifically, The areas of data management and the activities organisations undertake. Why it’s important to manage data, and what happens when data is not managed well. Lesson content, A PowerPoint/PDF presentation, ‘Data Management’ Excel Question workbook on ‘Data Management’ (for learners) Excel Answers workbook on ‘Data Management’ (for teachers) Planning document with learning intentions and success criteria For more information on the Data Science NPA, please see teachdata.science If you have any questions or feedback please email lessons@effini.com This lesson has been created by effini in partnership with The Data Lab. © 2023 This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 4,5 and 6. This lesson covers how to create new variables in Python, specifically, • what and how to to extract data to create a new variable • what and how to combine data to create a new variable Lesson content, A PowerPoint/PDF presentation, ‘Creating new variables by extracting & combining in Python’ Jupyter notebooks: ‘creating_variables_by_extracting_or_combining_with_answers.ipynb’ (for teachers), ‘creating_variables_by_extracting_or_combining.ipynb’ (for learners) Datasets used in the Jupyter notebooks: the datasets are stored online and imported by the Jupyter notebooks. Planning document with learning intentions and success criteria For more information on the Data Science NPA, please see teachdata.science This lesson has been created by effini in partnership with Data Education in Schools, The Data Lab and Data Skills for Work, with funding from the Scottish Government. © 2021. This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 4,5 and 6. This lesson covers how to summarise datasets in Python (part 1 of 2), specifically, summarise complete datasets perform summary calculations for single variables, such as the total, count, min/max and average values perform summary calculations for multiple variables Lesson content, A PowerPoint/PDF presentation, ‘Summarising datasets in Python (part 1)’ Jupyter notebooks: ‘summarising_datasets_with_answers_part_1.ipynb’ (for teachers) ‘summarising_datasets_part_1.ipynb’ (for learners) Datasets used in the Jupyter notebooks: the datasets are stored online and imported by the Jupyter notebooks. Planning document with learning intentions and success criteria For more information on the Data Science NPA, please see teachdata.science This lesson has been created by effini in partnership with Data Education in Schools, The Data Lab and Data Skills for Work, with funding from the Scottish Government. © 2021. This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 5 and 6. This lesson covers, Practise switching between wide and long datasets in Excel. This lesson follows on from the Data Science - Reshaping Datasets lesson, which is available through the effini TES shop. Lesson content, A PowerPoint/PDF presentation, ‘Practise reshaping datasets in Excel’ Excel Question workbook on ‘Practise reshaping datasets in Excel’ (for learners) Excel Answers workbook on ‘Practise reshaping datasets in Excel’ (for teachers) Planning document with learning intentions and success criteria The lesson has been designed for learners using Microsoft Excel on a Windows based machine. This lesson uses Power Query to reshape datasets. Power Query is currently only supported on Microsoft Excel when it is run on a Windows based machine. For more information on the Data Science NPA, please see teachdata.science If you have any questions or feedback on this lesson, please email lessons@effini.com This lesson has been created by effini in partnership with Data Education in Schools, The Data Lab and Data Skills for Work, with funding from the Scottish Government

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 4,5 and 6. This lesson covers what is involved in the analysis process, specifically, • what we mean by analysis • a structured way of performing analysis (the analysis steps) • how to understand data through visual inspection Lesson content, A PowerPoint/PDF presentation, ‘The analysis process’ Excel Question workbook on ‘The analysis process’ (for learners) Excel Answers workbook on ‘The analysis process’ (for teachers) Planning document with learning intentions and success criteria For more information on the Data Science NPA, please see teachdata.science This lesson has been created by effini in partnership with Data Education in Schools, The Data Lab and Data Skills for Work, with funding from the Scottish Government. © 2021. This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 5 and 6. This lesson covers, how pandas indexes work practise reshaping from long to wide and wide to long Lesson content, A PowerPoint/PDF presentation, ‘Practise reshaping datasets in Python’ 2 Jupyter notebooks: ‘practise_reshaping_data.ipynb’ (for learners) ‘practise_reshaping_data_with_answers.ipynb’ (for teachers) Planning document with learning intentions and success criteria For more information on the Data Science NPA, please see teachdata.science If you have any questions or feedback on this lesson, please email lessons@effini.com This lesson has been created by effini in partnership with Data Education in Schools, The Data Lab and Data Skills for Work, with funding from the Scottish Government

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 4,5 and 6. This lesson follows on from ‘Dataset understanding in Python’ (parts 1 &2) lessons which is available from the effini TES shop. This lesson allows learners to practise the skills covered in the Dataset understanding in Python lessons, specfically, • how to import a dataset without importing metadata • how to use a data dictionary to find out about a dataset • how to find the shape, size and format of datasets, using Python • how to find the data types of variables in a dataset, using Python • how to identify outliers and missing values in Python Lesson content, A PowerPoint/PDF presentation, ‘Practise Dataset Understanding in Python’ Jupyter notebooks: ‘practise_data_understanding withanswers.ipynb’ (for teachers), and 'practise_data_understanding.ipynb’ (for learners) Datasets used in the Jupyter notebook: the datasets are stored online and imported by the Jupyter notebook. Planning document with learning intentions and success criteria For more information on the Data Science NPA, please see teachdata.science This lesson has been created by effini in partnership with Data Education in Schools, The Data Lab and Data Skills for Work, with funding from the Scottish Government. © 2022. This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 4,5 and 6. This lessons follows on from ‘Dataset Understanding in Python (part 1)’ lesson which is available from the effini TES shop. This lesson continues to look at the data understanding step of the analysis step, specifically, • identification of outliers and missing values Lesson content, A PowerPoint/PDF presentation, ‘Dataset Understanding in Python (Part 2)’ Jupyter notebooks: ‘understanding_datasets_with_answers_part_2.ipynb’ (for teachers), and ‘understanding_datasets_part_2.ipynb’ (for learners) Datasets used in the Jupyter notebook: the datasets are stored online and imported by the Jupyter notebook. Planning document with learning intentions and success criteria For more information on the Data Science NPA, please see teachdata.science This lesson has been created by effini in partnership with Data Education in Schools, The Data Lab and Data Skills for Work, with funding from the Scottish Government. © 2022. This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 5 and 6. This lesson follows on from ‘Data cleansing in Excel’ and ‘Advanced data cleasning in Excel’ which are available from the effini TES shop. This lesson allows learners to practise the skills covered in the Data Cleansing part of the analysis process in Excel, specifically, • how to rename variables • how to drop unrequired rows and variables • how to drop duplicates • how to handle missing data and outliers Lesson content, A PowerPoint/PDF presentation, ‘Practise dataset cleansing in Excel’ Excel Question workbook on ‘Practise dataset cleansing in Excel’ (for learners) Excel/PDF Answers workbook on ‘Practise dataset cleansing in Excel’ (for teachers) Planning document with learning intentions and success criteria For more information on the Data Science NPA, please see teachdata.science This lesson has been created by effini in partnership with Data Education in Schools, The Data Lab and Data Skills for Work, with funding from the Scottish Government. © 2022. This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 5 and 6. This lesson follows on from ‘Data cleansing in Excel’ which is available from the effini TES shop. This lesson covers the advanced data cleansing part of the analysis process in Excel, specifically, • how to convert between different data types • how to fix strings • understand the reasons why there may be missing or outlying values, and how these reasons affect the ways in which we handle them Lesson content, A PowerPoint/PDF presentation, ‘Advanced dataset cleansing in Excel’ Excel Question workbook on ‘Advanced dataset cleansing in Excel’ (for learners) Excel/PDF Answers workbook on ‘Advanced dataset cleansing in Excel’ (for teachers) Planning document with learning intentions and success criteria For more information on the Data Science NPA, please see teachdata.science This lesson has been created by effini in partnership with Data Education in Schools, The Data Lab and Data Skills for Work, with funding from the Scottish Government. © 2022. This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 4,5 and 6. This lesson follows on from ‘The Analysis Process’ which is available from the effini TES shop. This lesson covers the data cleansing part of the analysis process in Excel, specifically, • how to drop unrequired rows and columns • what naming conventions are commonly used for variables and how to rename variables • how to remove duplicates • how to fix missing and outlying values that have already been identified • how to remove metadata Lesson content, A PowerPoint/PDF presentation, ‘Dataset cleansing in Excel’ Excel Question workbook on ‘Dataset cleansing in Excel’ (for learners) Excel/PDF Answers workbook on ‘Dataset cleansing in Excel’ (for teachers) Planning document with learning intentions and success criteria For more information on the Data Science NPA, please see teachdata.science This lesson has been created by effini in partnership with Data Education in Schools, The Data Lab and Data Skills for Work, with funding from the Scottish Government. © 2022. This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 4,5 and 6. This lesson follows on from ‘Dataset understanding in Excel’ lesson which is available from the effini TES shop. This lesson allows learners to practice the skills covered in the Dataset Understanding in Excel, specifically, • Using data dictionaries/metadata • Identifying size and shape • Identifying missing values and outliers Lesson content, A PowerPoint/PDF presentation, ‘Practise Dataset Understanding in Excel’ Question worksheet (for learners) on ‘Practise Dataset understanding in Excel’ in Excel Answers worksheet (for teachers) on ‘Practise Dataset understanding in Excel’ in Excel Planning document with learning intentions and success criteria For more information on the Data Science NPA, please see teachdata.science This lesson has been created by effini in partnership with Data Education in Schools, The Data Lab and Data Skills for Work, with funding from the Scottish Government. © 2022. This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Level 5 and 6. This lesson covers how to combine datasets, specifically, • what we mean by combining datasets • to add rows to a dataset by appending • to add columns to a dataset by joining • understand common types of joins Lesson content, A PowerPoint/PDF presentation, ‘Combining datasets’ Excel/PDF Question workbook on ‘Combining datasets’ (for learners) Excel/PDF Answers workbook on ‘Combining datasets’ (for teachers) Planning document with learning intentions and success criteria For more information on the Data Science NPA, please see teachdata.science If you have any questions or feedback please email lessons@effini.com This lesson has been created by effini in partnership with The Data Lab. © 2023 This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 4, 5 and 6. This lesson follows on from ‘The Analysis Process’ and the ‘Dataset cleansing in Python’ (part 1 & 2) which are available from the effini TES shop. This lesson covers the advanced data cleansing part of the analysis process in Python (part 1 of 2), specifically, • how to convert between data types Lesson content, A PowerPoint/PDF presentation, ‘Advanced data cleansing in Python (part 1)’ 2 Jupyter notebooks: ‘advanced_data_cleansing_part_1.ipynb’ (for learners) ‘advanced_data_cleansing_with_answers_part_1.ipynb’ (for teachers) Planning document with learning intentions and success criteria For more information on the Data Science NPA, please see teachdata.science This lesson has been created by effini in partnership with Data Education in Schools, The Data Lab and Data Skills for Work, with funding from the Scottish Government. © 2022. This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 4,5 and 6. This lessons is an Intro to Python for Data Science (part 2 of 2) ,covering, understand Python data types and data structures that are important for data science manipulate strings create and call Python functions call a Python object’s methods and access its properties perform a sequence of operations using method chaining Lesson content, Powerpoint presentation: ‘Introduction to Python for Data Science (Part 2)’ Jupyter notebooks: ‘intro_to_python_for_data_science_part_2.ipynb’ (for learners) ‘intro_to_python_for_data_science_with_answers_part_2.ipynb’ (for teachers) The Jupyter notebook for teachers contains answers to the tasks set for learners. Planning document with learning intentions and success criteria For more information on the Data Science NPA, please see teachdata.science If you have any questions or feedback on this lesson, please email lessons@effini.com This lesson has been created by effini in partnership with Data Education in Schools, The Data Lab and Data Skills for Work, with funding from the Scottish Government. © 2021. This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 4,5 and 6. This lessons is an Intro to Python for Data Science (part 1 of 2) ,covering, why Python is widely used in data science install, import and use Python packages understand how to get help when using Python name variables clearly and consistently Lesson content, Powerpoint presentation, ‘Introduction to Python for Data Science (Part 1)’ Jupyter notebooks: ‘intro_to_python_for_data_science_part_1.ipynb’ (for learners) ‘intro_to_python_for_data_science_with_answers_part_1.ipynb’ (for teachers) The Jupyter notebook for teachers contains answers to the tasks set for learners. Planning document with learning intentions and success criteria For more information on the Data Science NPA, please see teachdata.science If you have any questions or feedback on this lesson, please email lessons@effini.com This lesson has been created by effini in partnership with Data Education in Schools, The Data Lab and Data Skills for Work, with funding from the Scottish Government. © 2021. This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Level 6. This lesson covers the importance of data quality, specifically, What is high quality data and why its important How to assess and improve the quality of a dataset Lesson content, A PowerPoint/PDF presentation, ‘Importance of data quality’ Excel Question workbook on ‘Importance of data quality’ (for learners) Excel Answers workbook on ‘Importance of data quality’ (for teachers) Planning document with learning intentions and success criteria For more information on the Data Science NPA, please see teachdata.science If you have any questions or feedback please email lessons@effini.com This lesson has been created by effini in partnership with Data Education in Schools and Skills Development Scotland. © 2022 This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Level 6. This lesson covers how to care for your data, specifically, What are the different data types that need to be cared for How to create a data dictionary Lesson content, A PowerPoint/PDF presentation, ‘Caring for your data’ Excel Question workbook on ‘Caring for your data’ (for learners) Excel Answers workbook on ‘Caring for your data’ (for teachers) Planning document with learning intentions and success criteria For more information on the Data Science NPA, please see teachdata.science If you have any questions or feedback please email lessons@effini.com This lesson has been created by effini in partnership with Data Education in Schools and Skills Development Scotland. © 2022 This work is licensed under a CC BY-NC-SA 4.0 license.